Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
5G enabled Mobile Edge Computing security for Autonomous Vehicles
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2202.00005.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T23:41:02.925654Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
4
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 84630611-ae88-44d4-9792-796a88b6f7c8 · inbound
System Security Framework for 5G Advanced /6G IoT Integrated Terrestrial Network-Non-Terrestrial Network (TN-NTN) with AI-Enabled Cloud Security cites this paper.
System Security Framework for 5G Advanced /6G IoT Integrated Terrestrial Network-Non-Terrestrial Network (TN-NTN) with AI-Enabled Cloud Security 5G enabled Mobile Edge Computing security for Autonomous Vehicles
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T23:41:02.925654Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e245519d-f961-48ee-a809-dbc37817e0e4 · inbound
A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems cites this paper.
A Comparative Analysis of Machine Learning Models for Intrusion Detection in Intelligent Transport Systems 5G enabled Mobile Edge Computing security for Autonomous Vehicles
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T19:45:54.536926Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.